Multi-dimensional vehicle scheduling method and device, electronic equipment, storage medium and computer program product

Abstract

The application discloses a multi-dimensional vehicle scheduling method and device, electronic equipment, storage medium and computer program product, relates to the technical field of vehicle scheduling, and comprises the following steps: collecting basic information and state information of a transport vehicle and fixed facilities through an Internet of Things terminal and an industrial bus, establishing a mapping relationship between a device ID and a digital twin, planning a shortest path to a target node for an abnormal transport vehicle with information that does not affect driving, and otherwise identifying congestion for a normal transport vehicle and pushing scheduling information. The application can realize the precision, dynamic and intelligence of vehicle scheduling, and improve the transport efficiency and safety.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of vehicle scheduling. More particularly, the present application relates to a multi-dimensional vehicle scheduling method and device, electronic equipment, storage medium and computer program product. BACKGROUND

[0002] In the production and operation of open-pit mines, the scheduling of mine vehicles is of great importance, and its efficiency is directly related to the production progress, operation cost and work safety of the mine. However, the traditional scheduling method of mine vehicles has many limitations. First, the information collection is not comprehensive enough, and the static attributes (position, energy consumption, etc.) and dynamic attributes (running state, usage state) of the transport vehicles and fixed facilities (such as loading and unloading points, maintenance stations, etc.) cannot be effectively collected. For example, each mine vehicle has its unique performance parameters, such as load capacity, speed, fuel consumption, and current vehicle condition, etc. Different vehicles may perform differently when facing the same transportation task, and the state of the vehicle itself directly determines whether it can normally participate in the transportation work. If a vehicle breaks down, it will directly affect the execution of the transportation plan. Second, the disposal of faulty vehicles and low-oil / low-electricity vehicles relies on manual reporting, and the path planning is not reasonable enough, usually selected and judged by the driver manually, which may cause the vehicle to break down midway and affect the continuity of transportation. Third, the accuracy of congestion detection is insufficient, and congestion is determined by simply counting the number of vehicles without dynamic analysis and judgment based on spatial distribution characteristics, resulting in a high false positive rate, increasing the waiting time and operation cost of vehicles. Due to the interaction and influence of these multi-dimensional factors, the scheduling of mine vehicles in open-pit mines becomes extremely complex, and the traditional scheduling method cannot meet the efficient, safe and economic production needs, and there is an urgent need for a vehicle scheduling method that can consider various factors comprehensively. SUMMARY

[0003] The present application provides a multi-dimensional vehicle scheduling method and device, electronic equipment, storage medium and computer program product, which can realize precise and dynamic vehicle scheduling, improve transportation efficiency, reduce congestion misjudgment rate, ensure driving safety and enhance the intelligent level of scheduling.

[0004] In order to achieve these objects and other advantages in accordance with the present application, a multi-dimensional vehicle scheduling method is provided, comprising:

[0005] Step one: collecting basic information and state information through integration of Internet of Things terminal and industrial bus;

[0006] The basic information includes the static attributes of the transport vehicles and fixed facilities, and the fixed facilities include loading and unloading points, maintenance stations, charging and battery swapping stations, and fueling stations. The static attributes include the position and energy consumption of the transport vehicles and the position of the fixed facilities.

[0007] The status information includes the dynamic attributes of the transport vehicles and fixed facilities, including the operating status of the transport vehicles and the usage status of the fixed facilities.

[0008] At the same time, establish a mapping relationship between the unique equipment IDs of transport vehicles and fixed facilities and their digital twins;

[0009] Step 2: Obtain online transport vehicle information. When information that does not affect the operation of a transport vehicle is identified, the shortest path from the transport vehicle to the available fixed facility is calculated based on the static and dynamic attributes and with the fixed facility as the target node. When information that affects the operation of a transport vehicle is identified, the spatial distribution of the online normal transport vehicle location set is analyzed. When the vehicle density value of a specific route exceeds a preset density threshold, congestion warning and decentralized dispatch reminder information are pushed to the corresponding normal transport vehicle.

[0010] Preferably, the online transport vehicle information includes: vehicle long idling information, fault information, fuel level or battery level information, and the transport vehicle has information that does not affect driving, including: vehicle long idling, fault that does not affect driving, and fuel level or battery level not less than the threshold.

[0011] Preferably, when calculating the shortest path for the transport vehicle to the fixed facility, sections of road with heavy loads going uphill with a gradient value greater than a preset gradient threshold are excluded.

[0012] Preferably, the Floyd-Warshall algorithm is used to calculate the shortest path from the transport vehicle to the fixed facility, including:

[0013] 1) Loading and unloading points, maintenance stations, charging and battery swapping stations, and gas stations are designated as path nodes, and passable road segments between nodes are designated as path edges. Each edge is assigned an initial weight, which is the actual length of the road segment.

[0014] If the road segment is an unpaved surface, then the weight of that road segment will be adjusted to... W = L ×1.3, if it is a paved road surface, then further determine the slope of a certain road section. α If ≥5% of the traffic volume is high and the transport vehicles are operating in a heavily loaded uphill mode, the weight of that road segment will be adjusted accordingly.

[0015] ,

[0016] Conversely, the original length remains unchanged. W = L ;

[0017] in, W To correct the road segment weights, L This is the original road segment length. α Slope;

[0018] 2) initialize a distance matrix, each element in the matrix represents the initial distance from the starting point to the end point, the distance between the same nodes is 0, the distance between the nodes connected by the direct road section is the weight of the corresponding road section, and the distance between the nodes without direct connection is infinite;

[0019] From the first node, each node is taken as an intermediate node in turn, and the shortest path from all starting points to the end point is recalculated. If the path through the intermediate node is shorter than the previously recorded path, the distance matrix is updated;

[0020] When the distance matrix does not change after three consecutive iterations, the calculation is ended in advance;

[0021] When the distance matrix of the k th iteration is completely consistent with the distance matrix of the k -3、 k -2、 k -1th iteration, the calculation is stopped, k ≥3;

[0022] 3) extract the shortest path from the final distance matrix from the current position of the transport vehicle to the target station;

[0023] Automatically exclude the road section with a slope α ≥8% and the transport vehicle is in the heavy load uphill carrying mode;

[0024] The final path information is sent to the vehicle terminal instrument or mobile terminal APP of the transport vehicle.

[0025] Preferably, the transport path of the normal transport vehicle is dynamically planned, specifically including:

[0026] a) calculate the safety envelope space based on the speed of the normal transport vehicle, the safety envelope space is a long strip-shaped area with the normal transport vehicle as the center, extending 10 seconds of driving distance forward along the driving direction, extending 10 meters backward, and extending 5 meters left and right;

[0027] b) mark the abnormal transport vehicle as a virtual obstacle, and calculate the virtual obstacle space, calculate the driving area predicted for the next 30 seconds based on the speed of the abnormal transport vehicle, the virtual obstacle space is a long strip-shaped area with the driving area as the center, the length and width are twice the size of the abnormal transport vehicle, and the abnormal transport vehicle is a transport vehicle with information that does not affect driving;

[0028] c) when the safety envelope space of the normal transport vehicle overlaps with the space range of any virtual obstacle, the transport path of the normal transport vehicle is corrected, and the detour transport path is calculated based on the A* algorithm, the A* algorithm calculates the shortest path through the function f (n ) = g ( n )+ h ( n ) real-time update planning path, forming a detour path:

[0029]

[0030]

[0031] wherein, n is the path node number, i is the road section number of the mining area, 1≤ i ≤ m , L i is the actual length of the road section i , w i is the road surface weight, hardened road surface w i = 0.8, non-hardened road surface w i = 1.2, α i is the slope of the road section i , d (n，终点) is the Euclidean distance from the node n to the end point;

[0032] The detour path needs to meet:

[0033] The minimum distance between the detour path and the virtual obstacle space is ≥10 meters, and f ( n ) is minimized.

[0034] Preferably, the spatial distribution of the online normal transport vehicle position point set is analyzed, and the DBSCAN density clustering algorithm is used, including:

[0035] Set the neighborhood radius range and the minimum point number range, if the number of transport vehicles in the neighborhood radius range of a transport vehicle position point is greater than or equal to the minimum point number range, mark the position point as a core point;

[0036] Merge the core points that are connected to each other to form clusters, when the number of transport vehicles in a cluster exceeds the threshold range, preliminarily determine that there is a vehicle aggregation, and send congestion warning and dispersion scheduling reminder information.

[0037] Preferably, for any two core points p and q, if the neighborhood radius range of the core point p contains the core point q, it is determined that p and q are directly connected;

[0038] If there is a core point sequence, that is, there is a string of sequentially connected core points r1, r2, … r from core point p to q n If each adjacent core point satisfies: the neighborhood radius range of the previous core point contains the next core point, and the distance between the two does not exceed the neighborhood radius range, then p and q are determined to be indirectly connected;

[0039] All core points associated by direct connection or indirect connection are merged to form a cluster.

[0040] Preferably, after preliminary determination of vehicle aggregation, it further includes:

[0041] The fusion DeepSORT tracking algorithm continuously tracks the trajectory of the transport vehicles in the cluster, and if the number of transport vehicles in the cluster continuously exceeds the threshold range within the set time window, it is determined to be a vehicle aggregation, otherwise it is determined to be a transient aggregation and the aggregation determination is cancelled. According to the final aggregation determination result, congestion warning and dispersion scheduling reminder information is generated.

[0042] Preferably, the congestion warning and dispersion scheduling reminder information is pushed to the corresponding normal transport vehicles through the vehicle terminal instrument or mobile terminal APP and displayed;

[0043] The user feeds back confirmation information or abnormal report information through the vehicle terminal instrument or mobile terminal APP.

[0044] A multi-dimensional vehicle scheduling device adopts the method, and the device comprises:

[0045] An information collection and mapping module collects basic information and state information through Internet of Things terminals and industrial bus integration, and establishes a mapping relationship between the unique device ID of the transport vehicle and the digital twin of the fixed facility, which includes loading and unloading points, maintenance stations, charging and battery replacement stations, and fueling stations;

[0046] A transport vehicle path planning module obtains information of online transport vehicles, re-plans the path of abnormal transport vehicles that have information that does not affect driving, and uses the Floyd-Warshall algorithm to calculate the shortest path from the abnormal transport vehicle to the available fixed facility, wherein the path calculation excludes heavy load uphill sections with a slope exceeding a preset threshold;

[0047] A congestion detection and scheduling module is triggered when the transport vehicle has information that affects driving. The congestion detection and scheduling module analyzes the spatial distribution of the online normal transport vehicle position point set based on the DBSCAN density clustering algorithm, and when the vehicle density of a specific line exceeds a preset threshold, congestion warning and dispersion scheduling reminder information is pushed;

[0048] An information interaction module pushes the above-mentioned reminding information through a vehicle-mounted terminal or a mobile terminal APP, and receives user feedback confirmation or abnormal reporting information.

[0049] Preferably, the transport vehicle path planning module comprises:

[0050] An abnormal transport vehicle path dynamic planning unit uses a Floyd-Warshall algorithm to calculate the shortest path, and sequentially performs:

[0051] 1) Taking the loading and unloading points, maintenance stations, charging and battery swapping stations, and refueling stations as path nodes, taking the passable road sections between the nodes as path edges, and assigning an initial weight to each edge, the initial weight being the actual length of the road section;

[0052] If the road section is a non-hardened road surface, the weight of the road section is adjusted to W = L ×1.3, if it is a hardened road surface, further judgment is made, if the slope of a road section is α ≥5% and the transport vehicle is in a heavy load uphill carrying mode, the weight of the road section is corrected:

[0053] ,

[0054] otherwise, the original length remains unchanged W = L ;

[0055] wherein, W is the corrected road section weight, L is the original road section length, α is the slope;

[0056] 2) initializing a distance matrix, each element in the matrix representing the initial distance from the corresponding starting point to the end point, the distance between the same nodes being 0, the distance between the nodes connected by a direct road section being the weight of the corresponding road section, and the distance between the nodes without direct connection being infinite;

[0057] Starting from the first node, each node is sequentially taken as an intermediate node, and the shortest path from all starting points to the end point is recalculated, if the path through the intermediate node is shorter than the previously recorded path, the distance matrix is updated;

[0058] When the distance matrix no longer changes after three consecutive iterations, the calculation is ended in advance;

[0059] When the distance matrix of the k th iteration is completely consistent with the distance matrix of the k -3、 k -2、 k -1th iteration, the calculation is stopped, k ≥3;

[0060] 3) Extract the shortest path from the final distance matrix from the current position of the transport vehicle to the target site;

[0061] Automatically exclude the slope in the path α ≥ 8% and the transport vehicle is in the heavy load uphill carrying mode of the road section;

[0062] Issue the final path information to the vehicle terminal of the abnormal transport vehicle;

[0063] The normal transport vehicle path dynamic planning unit dynamically plans the transport path of the normal transport vehicle, and sequentially executes:

[0064] a) Calculate the safety envelope space based on the speed of the normal transport vehicle, which is a long strip-shaped area with the normal transport vehicle as the center, extending 10 seconds of driving distance forward along the driving direction, 10 meters backward, and 5 meters left and right;

[0065] b) Mark the abnormal transport vehicle as a virtual obstacle, and calculate the virtual obstacle space, which is a long strip-shaped area with the driving area predicted based on the speed of the abnormal transport vehicle for the next 30 seconds as the center, twice the length and width of the abnormal transport vehicle, and the abnormal transport vehicle is an abnormal transport vehicle with faults, less than 30% of the battery level, or less than 30% of the oil level;

[0066] c) When the safety envelope space of the normal transport vehicle overlaps with the space range of any virtual obstacle, the transport path of the normal transport vehicle is corrected, and the bypass transport path is calculated based on the A* algorithm, which updates the planned path in real time through the function f ( n ) = g ( n ) + h ( n ) to form the bypass path:

[0067]

[0068]

[0069] wherein, n is the path node number, i is the road section number of the mine area, 1≤ i ≤ m , L i is the actual length of the road section i , w i is the road surface weight, the hardened road surface w i= 0.8, non-hardened road surface w i = 1.2, α i is the slope of the road segment i , d (n，终点) is the Euclidean distance from the node n to the end point;

[0070] The detour path needs to meet the following conditions:

[0071] The minimum distance between the detour path and the virtual obstacle space is greater than or equal to 10 meters, and f ( n ) is minimized.

[0072] Preferably, the congestion detection and scheduling module comprises:

[0073] A clustering analysis unit sets a neighborhood radius range and a minimum point number range, marks the position point of any transport vehicle with a transport vehicle number greater than or equal to the minimum point number range in the neighborhood radius range as a core point, combines all core points associated by direct or indirect connection to form a cluster, and when the number of transport vehicles in the cluster exceeds a threshold range, preliminarily determines that the vehicles are gathered, and sends congestion warning and dispersion scheduling reminder information;

[0074] A tracking determination unit is triggered when the number of transport vehicles in the cluster exceeds the threshold range, and fuses the continuous trajectory tracking of the transport vehicles in the cluster by the DeepSORT tracking algorithm, and if the number of transport vehicles in the cluster continuously exceeds the threshold value within a set time window range, it is determined that the vehicles are gathered, otherwise it is determined that the vehicles are temporarily gathered and the determination is cancelled, and congestion warning and dispersion scheduling reminder information is generated according to the final gathering determination result.

[0075] An electronic device comprising at least one processor and a memory communicatively connected to the at least one processor, wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to cause the at least one processor to perform the method.

[0076] A storage medium having a computer program stored thereon, wherein the program is executed by a processor to implement the method.

[0077] A computer program product comprising a computer program, wherein the program is executed by a processor to implement the method.

[0078] The present application at least includes the following beneficial effects:

[0079] Firstly, the multi-dimensional vehicle scheduling method of the application can realize real-time monitoring of the real state of the transport vehicle through the integration of the Internet of Things terminal and the industrial bus to collect the basic information and state information of the transport vehicle and fixed facilities, and ensure that the abnormal transport vehicle can be identified and path planning can be performed in time by combining the mapping relationship between the equipment ID and the digital twin, based on the DBSCAN density clustering algorithm for spatial analysis of the vehicle position point set, through the core point recognition and connected cluster merging to preliminarily determine the vehicle aggregation, and combining the DeepSORT tracking algorithm to verify the aggregation persistence within the set time window, the instantaneous passing-by and the real congestion can be accurately distinguished, and the misjudgment rate of congestion warning can be significantly reduced, and the accuracy and efficiency of the transport scheduling can be ensured.

[0080] Secondly, for the abnormal transport vehicle, the multi-dimensional vehicle scheduling method of the application adopts the Floyd-Warshall algorithm to calculate the path, and dynamically adjusts the road section weight through the road type and slope, so that the path cost is more suitable for the actual energy consumption and risk, and at the same time, the iteration termination condition is clear, which reduces the risk of abnormal vehicle anchoring in the middle of the way; for the normal transport vehicle, the safety envelope space and the virtual obstacle space are defined, the safety boundary is clear, when the two overlap, the A* algorithm is used to calculate the detour path, which effectively avoids the collision between the normal vehicle and the abnormal vehicle, and improves the transport safety.

[0081] Thirdly, the multi-dimensional vehicle scheduling device of the application realizes the vehicle state monitoring, path planning, congestion processing and information interaction through modular design, the information collection and mapping module collects data through the Internet of Things and the industrial bus, realizes the real-time synchronization of the entity and the digital twin, the transport vehicle path planning module plans the path for the abnormal transport vehicle and plans the obstacle avoidance path for the normal transport vehicle, realizes the accurate identification and dispersed scheduling of the congestion, and the information interaction module completes the instruction pushing and feedback through the vehicle terminal or the mobile terminal APP, and forms a closed loop control.

[0082] Other advantages, objects, and features of the application will be apparent from the following description, and will be understood by those skilled in the art. DETAILED DESCRIPTION

[0083] The application will be further described in detail below with reference to examples, so that those skilled in the art can implement the application according to the description.

[0084] It should be understood that the terms such as "have", "contain" and "include" used herein do not exclude the presence or addition of one or more other elements or combinations thereof.

[0085] It should be noted that the methods in the following embodiments are all conventional methods unless otherwise specified, and therefore cannot be understood as limiting the application.

[0086] In the existing mine vehicle scheduling, the information collection does not fully integrate the multi-dimensional data of the transport vehicles and the fixed facilities, so it is impossible to fuse and improve the multi-dimensional data to solve the problems of abnormal vehicle path planning and congestion dynamic early warning. The present application provides a multi-dimensional vehicle scheduling method, comprising the following steps:

[0087] First, information collection and mapping. The basic information and state information are collected through the integration of Internet of Things terminals and industrial buses. The Internet of Things terminals refer to sensors and positioning devices used to collect vehicle and facility data, such as vehicle-mounted GPS and facility sensors. The industrial bus is a communication line connecting various devices for data integration. The basic information includes the static attributes of transport vehicles and fixed facilities, including loading and unloading points, maintenance stations, charging and battery swapping stations, and fueling stations. The static attributes refer to information that does not change rapidly with time, including the location, energy consumption of transport vehicles, and the location of fixed facilities. The state information includes the dynamic attributes of transport vehicles and fixed facilities, including the running state of transport vehicles and the use state of fixed facilities (such as whether the maintenance station is idle). After the collected data is integrated through the industrial bus, the system assigns a unique device ID to each vehicle and fixed facility, and establishes a mapping relationship between the unique device ID of the transport vehicle and the fixed facility and the digital twin. Through virtual mapping of physical devices, the virtual model can reflect the state of physical devices in real time, enabling simulation and analysis.

[0088] Second, abnormal vehicle path planning. Obtain online transport vehicle information, specifically, for example, vehicle long-idle information, fault information, oil or power information. When it is identified that the transport vehicle has information that does not affect driving, specifically, for example, long-idle, the vehicle engine continues to run for more than 10 minutes and the vehicle speed is ≤5 km / h, the user (driver) is reminded to pay attention, for example, the fault does not affect driving, or the power is ≥30% or the oil level is ≥30%. Based on the static attributes and dynamic attributes, the available maintenance station, charging and battery swapping station, or fueling station is taken as the target node, and the Floyd-Warshall algorithm is used to calculate the shortest path of the transport vehicle to reach. Preferably, the path calculation excludes heavy-load uphill road segments with a slope value greater than a preset slope threshold (such as 8%), which significantly increases energy consumption and fault risk and needs to be avoided in priority.

[0089] Specifically, the Floyd-Warshall algorithm is used to calculate the shortest path of the transport vehicle to reach the fixed facility, and the process is as follows:

[0090] First, determine the path nodes and edges. The current position of the abnormal transport vehicle, each available maintenance station, charging and battery swapping station, fueling station, and loading and unloading point are taken as path nodes, and the passable road segments between nodes are taken as path edges. The weight of each edge is initially set as the actual length of the corresponding road segment.

[0091] Secondly, initialize the distance matrix and perform iterative calculation, initialize a distance matrix, each element in the matrix represents the initial distance from one node to another node, the distance between the same nodes is set to 0, the distance between the nodes connected by the direct road segment is set to the weight of the corresponding edge (i.e. the actual length of the road segment), and the distance between the nodes without direct connection is set to infinity. Then, each node is taken as an intermediate node in turn, and the shortest path from all starting points to the end point is recalculated. If the path passing through the intermediate node is shorter than the currently recorded path, the corresponding distance value in the distance matrix is updated. Repeat this process until all nodes participate in the calculation as intermediate nodes, and the shortest path distance between nodes is obtained.

[0092] Finally, generate the path, extract the shortest path from the current position of the vehicle to the target site from the final distance matrix, automatically exclude the road segment with a slope α≥8% and the vehicle in a heavy load uphill state, and send the path information to the abnormal transport vehicle terminal.

[0093] When the information affecting driving is identified, specifically, for example, the fault affecting driving, or the power <30% or the oil level <30%, it is possible to cause congestion, and congestion detection and reminding need to be performed. The vehicle positioning terminal uploads the position information of the normal transport vehicle to the scheduling server in real time, the server runs the DBSCAN density clustering algorithm to analyze the spatial distribution of the online normal transport vehicle position point set, sets the neighborhood radius (such as 50 meters) and the minimum number of points (such as 5 vehicles), marks the core point when the number of transport vehicles in the neighborhood of a certain position point reaches the standard, and connects the core points to form a cluster. When the vehicle density value of a specific line exceeds the preset density threshold value, that is, the number of transport vehicles in the cluster exceeds the preset threshold value (such as 10 vehicles), the congestion warning and dispersion scheduling reminder information is pushed to the corresponding normal transport vehicle.

[0094] In the above technical solution, the static and dynamic information of the transport vehicle and the fixed facility is integrated through the Internet of Things terminal and the industrial bus, and the mapping of the equipment ID and the digital twin is also established, which enables the scheduling to obtain comprehensive and accurate data, avoiding the scheduling deviation caused by the scattered and inaccurate information in the past; when the vehicle with the fault that does not affect driving, the power or the oil is insufficient, the Floyd-Warshall algorithm is used to calculate the shortest path, and the heavy load uphill road segment with a slope exceeding the preset threshold value is deliberately excluded, which can reduce the possibility of problems of these vehicles in the high energy consumption and high risk road segment, make them more stable to reach the maintenance or supply site, and reduce the probability of anchoring on the way and delaying transportation; the position distribution of the normal vehicle is analyzed by DBSCAN density clustering, and when the vehicle density exceeds the threshold value, the congestion warning and dispersion reminding is pushed in time, which can discover the clustering situation as soon as possible and dredge, reduce the waiting time caused by the vehicles blocking together, make the overall transportation more smooth, and improve the scheduling efficiency and safety.

[0095] Further, the above Floyd-Warshall algorithm does not fully consider the influence of road type and slope on transportation, and simply calculating the path according to length may cause abnormal vehicles to travel on heavy load uphill and other road sections with high energy consumption and high risk, and the iteration termination condition is not clear, which affects the efficiency and safety of abnormal vehicles reaching the target station. In another technical solution, the improved Floyd-Warshall algorithm is used to calculate the shortest path, including:

[0096] 1) The loading and unloading points, maintenance stations, charging and changing stations, and refueling stations are taken as path nodes. In the path node setting, the charging and changing stations and the refueling stations are included according to the vehicle type (new energy vehicles correspond to charging and changing stations, and oil vehicles correspond to refueling stations), so as to avoid irrelevant node interference. The passable road sections between the nodes are taken as path edges, and each edge is assigned an initial weight, which is the actual length of the road section. If there are multiple passable road sections (such as different branches), each road section corresponds to a path edge, and is respectively assigned an actual length as the initial weight. If there is only one passable road section, the road surface is unique, and corresponds to one edge.

[0097] The weight is corrected to reflect the actual driving cost (energy consumption, risk, and loss) of the road section. If the road section is a non-hardened road surface, the driving resistance is large, the vehicle wears out quickly, and the energy consumption is high. Then the weight of the road section is adjusted to W = L ×1.3, and the hardened road surface is preferred to reduce the increase of energy consumption and part wear caused by vehicle bumping.

[0098] If it is a hardened road surface, further judgment is made. If the slope of a road section is α ≥5% and the transport vehicle is in heavy load uphill carrying mode, the greater the slope, the more power consumption and the higher the risk of failure. The slope influence is quantified by a proportionality coefficient (α / 5%) to make the weight increase with the increase of the slope, and the gentle road section is preferred to reduce the risk of midway anchoring. Then the weight of the road section is corrected:

[0099] ,

[0100] otherwise, the original length remains unchanged W = L ;

[0101] wherein, W is the corrected road section weight, L is the original road section length, α is the slope;

[0102] 2) Initialize a distance matrix, and update the path with each node as the intermediate node in turn. Stop when the matrix does not change for three consecutive iterations. Specifically:

[0103] Each element in the matrix represents the initial distance from the corresponding starting point to the ending point, the distance between the same nodes is 0, the distance between the nodes connected by the direct road section is the weight of the corresponding road section, and the distance between the nodes without direct connection is infinite;

[0104] Starting from the first node, each node is sequentially t As an intermediate node, the shortest path from all starting points to the ending point is recalculated a If the path through the intermediate node is shorter than the previously recorded path, the distance matrix is updated, that is, if the total weight of the path b → a → t is less than the weight of the current b → a , the distance from b to a in the matrix is updated to the total weight of b → a → t ; b When the distance matrix does not change after three consecutive iterations, it is considered that the shortest path between all nodes has been found, and the calculation is ended in advance;

[0105] When the distance matrix of the

[0106] th iteration is completely consistent with the distance matrix of the k -3、 k -2、 k -1th iteration, the calculation is stopped, k ≥3; k

[0107] 3) Extract the shortest path from the final distance matrix from the current position of the transport vehicle to the target station;

[0108] Automatically exclude the road section with a slope α ≥8% and the transport vehicle in the heavy load uphill carrying mode in the path;

[0109] The final path information is sent to the vehicle terminal instrument or mobile terminal APP of the transport vehicle.

[0110] In the above technical solution, the path planning not only considers the influence of road type and slope on energy consumption, but also ensures the calculation efficiency through the iteration termination condition, reduces the risk of anchoring in the middle of the way, ensures that they reach the maintenance station or supply point more stably, and the clear iteration termination condition reduces the waste of computing resources, can quickly respond when multiple transport vehicles are dispatched at the same time, and overall improves the practicality and reliability of the path planning.

[0111] ​When a temporary obstacle (an abnormal transport vehicle) encounters a normal transport vehicle while it is in motion, there is a risk of collision if the path is not automatically adjusted in time based on the obstacle. Another technical solution involves dynamically planning the transport path of the normal transport vehicle, specifically including:

[0112] a) Calculate the safety envelope space based on the speed of normal transport vehicles, that is, the dynamic safety area centered on the vehicle, which is adjusted in real time according to the vehicle type and speed. The safety envelope space is a long strip-shaped area centered on the normal transport vehicle, extending forward for the next 10 seconds along the direction of travel, extending backward for 10 meters, and extending to the left and right for 5 meters.

[0113] b) Mark abnormal transport vehicles as virtual obstacles and calculate the virtual obstacle space, which is adjusted in real time according to vehicle type and speed. That is, the obstacle (abnormal transport vehicle) is transformed into a virtual no-entry zone to prevent normal transport vehicles from approaching. Calculate the driving area for the next 30 seconds based on the speed prediction of the abnormal transport vehicle. The virtual obstacle space is a long strip area centered on the driving area with a length and width twice that of the abnormal transport vehicle. The abnormal transport vehicle is a transport vehicle with a fault that does not affect driving, or with a battery level of less than 30% or an oil level of less than 30%.

[0114] c) When the safety envelope space of a normal transport vehicle overlaps with the spatial range of any virtual obstacle, the transport path of the normal transport vehicle is corrected, and a detour transport path is calculated based on the A* algorithm, wherein the A* algorithm uses a function... f ( n ) = g ( n )+ h ( n The planned route is updated in real time to generate detour routes. f ( n (From the starting point) a’ go through n To the finish line b’ The estimated total cost, g ( n (From the starting point) a’ To the current node n The actual cost, h ( n ) is a slave node n To the finish line b’ The estimated cost;

[0115]

[0116]

[0117] The mining area has a large number of unpaved roads (dirt roads, gravel roads), throughw i The weighting algorithm can preferentially select a low-resistance hardened road surface, reduce vehicle wear and energy consumption, and the slope (especially the heavy load up and down slope) directly affects the power consumption. The greater the absolute value of the slope, the higher the energy consumption. The actual cost of the steep slope section is amplified. The algorithm avoids high-energy consumption steep slopes. In addition, the mine path is mostly non-straight (limited by terrain and facilities), which amplifies the actual path length, making it closer to the actual path and meeting the needs of real-time scheduling in the mine. Among them, n is the path node number, i is the road section number of the mine, 1≤ i ≤ m , L i is the actual length of the road section i , w i is the road surface weight, the hardened road surface w i = 0.8, the non-hardened road surface w i = 1.2, α i is the slope of the road section i , d (n，终点) is the Euclidean distance from the node n to the end point.

[0118] The detour path needs to meet:

[0119] When the A* algorithm searches for a path, a distance check is added to each node, which meets the minimum distance between the detour path and the virtual obstacle space ≥ 10 meters. For all candidate paths that meet the condition, the total cost is calculated by the cost function f ( n ) = g ( n )+ h ( n ) of the A* algorithm, and f ( n ) is minimized.

[0120] In the above technical solution, by defining the safety envelope space and the virtual obstacle space, the safety boundary is defined to avoid close contact between normal vehicles and abnormal vehicles. The path is corrected in real time by the A* algorithm to ensure safe and efficient detouring. The dynamic response mechanism does not require manual intervention, and the safety and smoothness of normal vehicle transportation in complex scenarios are improved.

[0121] In order to adapt to complex environments such as mining areas, avoid misjudging scattered vehicles as core points, or missing the actual gathering, in another technical solution, specifically, according to the scene characteristics such as the width of the mining area road and the size of the vehicle, the neighborhood radius range and the minimum point number range are set, then the number of vehicles in the neighborhood radius range of each normal transport vehicle position point is counted, if the number is greater than or equal to the minimum point number range, the position point is marked as a core point;

[0122] The core points that are connected to each other are merged to form a cluster. For example, the neighborhood of vehicle A includes vehicle B, and the neighborhood of vehicle B includes vehicle C. Vehicle A, B and C are merged into a cluster. When the number of transport vehicles in the cluster exceeds the threshold range, it is preliminarily determined that there is a vehicle gathering, and congestion warning and dispersion scheduling reminder information is sent.

[0123] In the above technical solution, the spatial gathering state of the vehicle can be accurately identified, and misjudgment caused by total number statistics can be avoided. For example, the situation of vehicle gathering and scattered distribution can be distinguished, the congestion warning is more accurate, and invalid scheduling is reduced. In this way, the user (driver) can receive reliable shunting reminders in a timely manner, avoid waiting caused by gathering, and the overall transportation is more smooth.

[0124] In order to further define the range of the cluster, avoid splitting the core points that should belong to the same gathering area into different clusters, or incorrectly merging unrelated core points, which affects the accurate judgment of the vehicle gathering range and further causes congestion warning deviation, in another technical solution, for any two core points p and q, if the neighborhood radius range of core point p contains core point q, then p and q are determined to be directly connected;

[0125] If there is a core point sequence, that is, there is a string of sequentially connected core points r1, r2, …r n , where each adjacent core point (such as r1, r2, …r n ) satisfies: the neighborhood radius range of the previous core point contains the next core point, and the distance between them does not exceed the neighborhood radius range, then p and q are determined to be indirectly connected;

[0126] All core points associated by direct connection or indirect connection are merged to form a cluster, so as to clearly define the boundary of the gathering area.

[0127] In the above technical solution, by defining the core point connection rule, the range of the cluster can be accurately defined, and cluster splitting or incorrect merging caused by ambiguous connection standards can be avoided. This makes the division of the vehicle gathering area more in line with the actual spatial distribution, ensures that the congestion warning accurately reflects the real gathering situation, makes the dispersion scheduling reminder more targeted, reduces invalid scheduling caused by inaccurate gathering range judgment, and improves the traffic efficiency of the transportation line.

[0128] In order to further improve the accuracy of analysis and judgment, for example, the cluster formed by the temporary intersection of vehicles (such as meeting and short stay) meets the density condition of aggregation, but belongs to the transient state, if it is directly judged as congestion and dispatched, it will lead to invalid operation, increase the burden of drivers and transportation interference, in another technical solution, after preliminary judgment of vehicle aggregation, it also includes:

[0129] The DeepSORT tracking algorithm is fused to continuously track the trajectories of the transport vehicles in the cluster, real-time data such as the position and speed of each transport vehicle in the cluster are obtained, and the trajectory of each transport vehicle is continuously recorded. The number of vehicles in the cluster is counted every 30 seconds, and if the number of transport vehicles in the cluster continuously exceeds the threshold range within a set time window (such as 3 minutes), it is determined that the vehicles are aggregated, otherwise if the number of vehicles at a certain moment is lower than the threshold, it is determined that the aggregation is transient and the aggregation determination is cancelled. According to the final aggregation determination result, congestion warning and dispersion scheduling reminder information is generated.

[0130] In the above technical solution, through continuous tracking in the time dimension, real congestion and transient aggregation can be accurately distinguished, avoiding misjudgment caused by temporary vehicle intersection, so that the congestion warning and scheduling reminder pushed are more in line with the actual situation, reducing unnecessary detour or shunting operation of drivers, reducing transportation interference, making scheduling more accurate and efficient, and ensuring the smoothness of line traffic.

[0131] In order to improve the timeliness of information transmission and interaction, in another technical solution, the congestion warning and dispersion scheduling reminder information is pushed to the corresponding normal transport vehicles through the vehicle terminal instrument or mobile terminal APP and displayed, for example, the vehicle terminal instrument displays in the form of text scrolling + icon flashing, and the mobile terminal APP pops up a prompt box with vibration;

[0132] After the user (driver) views, the vehicle terminal instrument or mobile terminal APP feedback confirmation information is fed back to the scheduling system, and if no congestion or other abnormalities are found on site, abnormal report information can be submitted to the scheduling system.

[0133] In the above technical solution, multiple modes of pushing ensure that the user (driver) can see the reminder in time and avoid information omission; feedback information receiving state to the scheduling system, when encountering abnormalities, it can be quickly verified and adjusted, reducing invalid operation caused by information difference. The present application also provides a multi-dimensional vehicle scheduling device, which adopts the method, and the device comprises:

[0134] An information collection and mapping module collects basic information and state information through integration of an Internet of Things terminal (such as a vehicle-mounted GPS, a facility sensor) and an industrial bus, the basic information includes static attributes of a transport vehicle and a loading and unloading point, a maintenance station, a charging and battery swapping station and a refueling station, including the position of the transport vehicle and the fixed facility, the state information includes dynamic attributes of the transport vehicle and the loading and unloading point, the maintenance station, the charging and battery swapping station and the refueling station, including the running state of the transport vehicle and the use state of the fixed facility (such as whether the maintenance station is idle), and a mapping relationship between the unique device ID of the transport vehicle and the fixed facility and the digital twin is established;

[0135] A transport vehicle path planning module obtains online transport vehicle information, specifically, for example, vehicle long idle information, fault information, oil or electricity information, for the transport vehicle, there is information that does not affect driving, including: vehicle long idle, fault that does not affect driving, oil or electricity is not less than a threshold, specifically, the long idle vehicle reminds the user (driver) to pay attention, the abnormal transport vehicle with a fault or an electricity less than 30% or an oil level less than 30% re-plans a path, taking a usable maintenance station, a charging and battery swapping station (new energy vehicle) or a refueling station (oil vehicle) as a target node, and using a Floyd-Warshall algorithm to calculate the shortest path from the abnormal transport vehicle to the usable maintenance station, the charging and battery swapping station or the refueling station, wherein the path calculation excludes a heavy load uphill road section with a slope exceeding a preset threshold;

[0136] A congestion detection and scheduling module is triggered when the transport vehicle has information that affects driving, the congestion detection and scheduling module analyzes the spatial distribution of the online normal transport vehicle position point set based on a DBSCAN density clustering algorithm, and when the vehicle density of a specific line exceeds a preset threshold, congestion warning and dispersion scheduling reminder information is pushed;

[0137] An information interaction module pushes the above reminder information through a vehicle-mounted terminal or a mobile terminal APP, and receives user feedback confirmation or abnormal reporting information, forming information interaction.

[0138] In the above technical solution, through the whole process design of the information collection and mapping module, the transport vehicle path planning module, the congestion detection and scheduling module and the information interaction module, multi-dimensional fusion of vehicle state, facility information and road condition data is realized, abnormal vehicle path planning ensures efficient arrival at a supply / maintenance station, reduces the risk of anchor throwing, congestion detection reduces the misjudgment rate by combining spatial density and time persistence, information interaction closes the loop to ensure that the scheduling instruction is implemented, and the safety, efficiency and intelligent level of transportation in a complex scene such as a mine are overall improved.

[0139] In another technical solution, the transport vehicle path planning module includes:

[0140] An abnormal transportation vehicle path dynamic programming unit uses a Floyd-Warshall algorithm to calculate the shortest path, and sequentially performs:

[0141] 1) The loading and unloading points, maintenance stations, charging and battery changing stations, and refueling stations are taken as path nodes, the passable road sections between the nodes are taken as path edges, and an initial weight is assigned to each edge, which is the actual length of the road section;

[0142] If the road section is a non-hardened road surface, the weight of the road section is adjusted to W = L ×1.3, the hardened road surface is preferentially selected to reduce the part wear and energy consumption increase caused by vehicle jolt;

[0143] If it is a hardened road surface, further judgment is made, if the slope of a road section is α ≥5% and the transportation vehicle is in a heavy load uphill carrying mode, the weight of the road section is corrected:

[0144] ,

[0145] otherwise, the original length remains unchanged W = L , the gentle road section is preferentially selected to reduce the risk of mid-way anchoring;

[0146] wherein, W is the corrected road section weight, L is the original road section length, α is the slope;

[0147] 2) An initial distance matrix is initialized, each element in the matrix represents the initial distance from the corresponding starting point to the end point, the distance between the same nodes is 0, the distance between the nodes connected by the direct road section is the weight of the corresponding road section, and the distance between the nodes without direct connection is infinite;

[0148] Starting from the first node, the path is updated with each node as the intermediate node, specifically, each node is sequentially taken as the intermediate node, and the shortest path from all starting points to the end point is recalculated, if the path passing through the intermediate node is shorter than the previously recorded path, the distance matrix is updated;

[0149] When the distance matrix no longer changes after three consecutive iterations, the calculation is ended in advance;

[0150] When the distance matrix of the k th iteration is completely consistent with the distance matrix of the k -3、 k -2、 k -1th iteration, the calculation is stopped, k ≥3;

[0151] 3) Extract the shortest path from the final distance matrix from the current position of the transport vehicle to the target site;

[0152] Automatically exclude the slope in the path α ≥ 8% and the transport vehicle is in the heavy load uphill carrying mode of the road section;

[0153] Distribute the final path information to the vehicle terminal of the abnormal transport vehicle;

[0154] The normal transport vehicle path dynamic planning unit dynamically plans the transport path of the normal transport vehicle, which is executed in turn:

[0155] a) Calculate the safety envelope space based on the speed of the normal transport vehicle, which is adjusted in real time with the vehicle type and speed. The safety envelope space is a long strip-shaped area with the normal transport vehicle as the center, extending 10 seconds of driving distance forward along the driving direction, 10 meters backward, and 5 meters left and right;

[0156] b) Mark the abnormal transport vehicle as a virtual obstacle and calculate the virtual obstacle space, which is adjusted in real time with the vehicle type and speed. Calculate the driving area of the future 30 seconds based on the speed prediction of the abnormal transport vehicle. The virtual obstacle space is a long strip-shaped area with the driving area as the center, twice the length and width of the abnormal transport vehicle, which is the virtual obstacle space. The abnormal transport vehicle is an abnormal transport vehicle with faults, less than 30% of the battery level, or less than 30% of the oil level;

[0157] c) When the safety envelope space of the normal transport vehicle overlaps with the space range of any virtual obstacle, the transport path of the normal transport vehicle is corrected, and the bypass transport path is calculated based on the A* algorithm, which updates the planned path in real time through the function f ( n ) = g ( n ) + h ( n ) to form the bypass path:

[0158]

[0159]

[0160] wherein, n is the path node number, i is the road section number of the mine area, 1≤ i ≤ m , L i is the actual length of the road section i , w i is the road surface weight, hardening roadw i = 0.8, non-hardened road surface w i = 1.2, α i is the slope of the road segment i , d (n，终点) is the Euclidean distance from the node n to the end point;

[0161] The detour path needs to meet:

[0162] The minimum distance between the detour path and the virtual obstacle space is greater than or equal to 10 meters, and f ( n ) is minimized.

[0163] In the above technical solution, the abnormal transport vehicle path dynamic programming unit considers the influence of road surface type and slope on energy consumption, reduces the risk of abnormal transport vehicle breakdown through weight correction and high-risk road segment exclusion, and the normal transport vehicle path dynamic programming unit combines dynamic safety boundary and A* algorithm to realize active obstacle avoidance for abnormal transport vehicle, ensuring safe and efficient detour.

[0164] In another technical solution, the congestion detection and scheduling module comprises:

[0165] A clustering analysis unit sets a neighborhood radius range and a minimum point range according to scene characteristics such as mine road width and vehicle size, marks the position point of any transport vehicle in the neighborhood radius range as a core point when the number of transport vehicles in the neighborhood radius range is greater than or equal to the minimum point range, and merges all core points associated by direct or indirect connection to form a cluster. Specifically, mutual neighborhood points are determined as direct connection, and core point sequences are connected to determine indirect connection. When the number of transport vehicles in the cluster exceeds the threshold range, it is preliminarily determined that the vehicles are gathered, and congestion warning and dispersion scheduling reminder information is sent.

[0166] A tracking determination unit is triggered when the number of transport vehicles in the cluster exceeds the threshold range, and fuses the DeepSORT tracking algorithm for continuous trajectory tracking of transport vehicles in the cluster, sets a time window range, and determines that the vehicles are gathered if the number of transport vehicles in the cluster continuously exceeds the threshold within the set time window range, and determines that the vehicles are temporarily gathered and cancels the determination otherwise. According to the final gathering determination result, congestion warning and dispersion scheduling reminder information is generated.

[0167] In the above technical solution, the configuration of neighborhood radius and time window adapts to the traffic capacity of different road segments, solving the problem of easy misjudgment of instantaneous meeting in traditional congestion detection relying only on spatial density, and DeepSORT tracking improves the trajectory association accuracy in complex scenes, accurately identifies the spatial gathering state of vehicles, and improves road traffic efficiency.

[0168] The application further provides an electronic device, comprising at least one processor and a memory connected with the at least one processor in communication, wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the method. The electronic device can be any terminal device, including a mobile phone, a notebook computer, a desktop computer, a tablet computer, a PDA (Personal Digital Assistant), a POS (Point of Sales), a vehicle-mounted computer, etc.

[0169] The application further provides a storage medium, which stores a computer program executable by a processor to implement the method. Through the description of the above embodiments, those skilled in the art can clearly understand that the application can be implemented by means of software and necessary general hardware, and of course can also be implemented by means of special hardware including special integrated circuits, special CPUs, special memories, special components, etc. Generally, any function completed by a computer program can be easily implemented by corresponding hardware, and the specific hardware structure for implementing the same function can also be various, such as analog circuits, digital circuits or special circuits, etc. However, for the application, software program implementation is a better embodiment in most cases. Based on such understanding, the technical solutions of the application or the part that contributes to the prior art can be embodied in the form of a software product, which is stored in a readable storage medium, such as a floppy disk, a U disk, a mobile hard disk, a ROM (Read-Only Memory), a RAM (Random Access Memory), a magnetic disk or an optical disk, etc., and includes a plurality of instructions to enable a computer device (which can be a personal computer, a server or a network device, etc.) to perform the method described in each embodiment of the application.

[0170] The present application also provides a computer program product comprising a computer program which, when executed by a processor, implements the method described. The computer program (which can also be referred to or described as a program, software, a software application, a module, a software module, a script, or code) can be written in any form of programming language, including compiled or interpreted languages, or declarative or procedural languages, and can be deployed in any form, including as a stand-alone program or as a module, component, subroutine, or other unit suitable for use in a computing environment. A computer program may, but need not, correspond to a file in a file system. A program can be stored in a portion of a file that holds other programs or data, e.g., one or more scripts stored in a markup language document, in a single file dedicated to the program in question, or in multiple coordinated files, e.g., files that store one or more modules, sub programs, or portions of code. The computer program can be deployed for execution on one computer or on multiple computers that are located at one site or distributed across multiple sites and are connected by a communication network. The processes and logic flows described in this specification can be performed by one or more programmable computers executing one or more computer programs to perform functions by operating on input data and generating output.

[0171] The number of devices and the processing size illustrated herein are used to simplify the explanation of the present application. Applications, modifications, and variations of the present application that are obvious to those skilled in the art are to be considered within the scope of the present application.

[0172] While embodiments of the application have been disclosed in connection with the specified embodiments, as illustrated above and discussed above, it will be apparent to those skilled in the art that numerous modifications, additions and omissions can be made without departing from the general concept and scope of the application. Accordingly, it is intended that all such modifications, additions and omissions be considered as within the scope of the application as defined in the following claims and their equivalents.

Claims

1. A multi-dimensional vehicle dispatching method, characterized by, The application relates to a method for dynamically planning a transport path of a transport vehicle, and belongs to the field of intelligent transportation. The method comprises the following steps: Step 1: collecting basic information and state information through integration of an Internet of Things terminal and an industrial bus; The basic information comprises static attributes of the transport vehicle and fixed facilities, and the fixed facilities comprise loading and unloading points, maintenance stations, charging and battery swapping stations and refueling stations, and the static attributes comprise the position, energy consumption of the transport vehicle and the position of the fixed facilities; The state information comprises dynamic attributes of the transport vehicle and the fixed facilities, and the dynamic attributes comprise the running state of the transport vehicle and the use state of the fixed facilities; Meanwhile, a mapping relationship between the unique device ID of the transport vehicle and the fixed facilities and a digital twin is established; Step 2: obtaining online transport vehicle information, when it is identified that the transport vehicle has information that does not affect driving, taking the available fixed facilities as target nodes, the shortest path of the transport vehicle to the fixed facilities is calculated based on the static attributes and the dynamic attributes; when it is identified that the transport vehicle has information that affects driving, the spatial distribution of the position point set of the online normal transport vehicle is analyzed, when the vehicle density value of a specific line exceeds a preset density threshold value, congestion early warning and dispersion scheduling reminder information are pushed to the corresponding normal transport vehicle; The online transport vehicle information comprises vehicle long idling information, fault information, oil or electricity information, and the information that the transport vehicle has does not affect driving, and comprises the following: vehicle long idling, fault that does not affect driving, oil or electricity not less than a threshold value; The shortest path of the transport vehicle to the fixed facilities is calculated by using the Floyd-Warshall algorithm, and the algorithm comprises the following steps: If the road section is a non-hardened road, the weight of the road section is adjusted to W = L × 1.3, if it is a hardened road, further judgment is made, if the slope of a road section is α ≥ 5% and the transport vehicle is in heavy load uphill carrying mode, the weight of the road section is corrected: ， Otherwise, the original length is maintained W L ;​ wherein, W is the modified link weight, L is the original link length, α is the slope; 1) taking the loading and unloading points, the maintenance stations, the charging and battery swapping stations and the refueling stations as path nodes, taking the passable road sections between the nodes as path edges, and giving each edge an initial weight, which is the actual length of the road section; 2) initializing a distance matrix, each element in the matrix representing the initial distance from the corresponding starting point to the terminal point, the distance between the same nodes being 0, the distance between the nodes connected by a direct road section being the weight of the corresponding road section, and the distance between the nodes without direct connection being infinite; Starting from the first node, each node is taken as an intermediate node in turn, and the shortest path of all starting points to terminal points is recalculated, if the path passing through the intermediate node is shorter than the previously recorded path, the distance matrix is updated; When the distance matrix of the 1 k st iteration is identical to the distance matrix of the 1 k st iteration, the calculation is stopped, k st iteration, k st iteration, k ≥ 3; When the distance matrix does not change after three consecutive iterations, the calculation is ended in advance; Automatic exclusion of slopes in the route α ≥ 8% and the transport vehicle is in a heavy uphill load mode on the section of the route; 3) extracting the shortest path from the current position of the transport vehicle to the target station from the final distance matrix; The final path information is sent to the vehicle-mounted terminal instrument or the mobile terminal APP of the transport vehicle; The transport path of the normal transport vehicle is dynamically planned, and the method comprises the following steps: a) calculating a safety envelope space based on the speed of the normal transport vehicle, the safety envelope space being a long strip-shaped region with the normal transport vehicle as the center, extending 10 seconds of driving distance forward along the driving direction, extending 10 meters backward, and extending 5 meters leftward and rightward; b) marking the abnormal transport vehicle as a virtual obstacle, and calculating a virtual obstacle space, the virtual obstacle space being a long strip region centered on a driving area predicted for the next 30 seconds based on the speed of the abnormal transport vehicle, the long strip region having a length and a width each being twice the abnormal transport vehicle 2, the abnormal transport vehicle being a transport vehicle having information that does not affect driving; c) when the safety envelope space of the normal transport vehicle overlaps with the spatial range of any virtual obstacle, then the transport path of the normal transport vehicle is corrected, a detour transport path is calculated based on the A* algorithm, the A* algorithm is calculated by the function f ( n ) = g ( n )+ h ( n ) real-time update of the planned path, forming a detour path: wherein, n is the path node number, i is the section number of the mine, 1≤ i ≤ m , L i is the actual length of the section i , w i is the road surface weight, 0.8 for hardened road surface w i = 1.2 for non-hardened road surface w i , α i is the slope of the section i , d (n，终点) is the Euclidean distance from the node n to the end point; The detour path needs to meet: The minimum distance of the detour path to the virtual obstacle space is ≥ 10 meters and makes f ( n ) minimum.

2. The multi-dimensional vehicle dispatching method of claim 1, wherein, The spatial distribution of the online normal transport vehicle position point set is analyzed, and the DBSCAN density clustering algorithm is used, including: Set the neighborhood radius range and the minimum point range, if the number of transport vehicles in the neighborhood radius range of a certain transport vehicle position point is greater than or equal to the minimum point range, mark the position point as a core point; Merge the mutually connected core points to form a cluster, and when the number of transport vehicles in the cluster exceeds the threshold range, preliminarily determine that the vehicles are aggregated, and send congestion warning and dispersion scheduling reminder information.

3. The multi-dimensional vehicle scheduling method of claim 2, wherein For any two core points p and q, if the neighborhood radius range of core point p contains core point q, it is determined that p and q are directly connected; If there exists a core point sequence, i.e. there exists a string of sequentially connected core points r1, r2, …r n wherein each adjacent core point satisfies: the neighborhood radius range of the former core point contains the latter core point, and the distance between the two does not exceed the neighborhood radius range, then it is determined that p and q are indirectly connected; All core points associated by direct or indirect connection are merged to form a cluster.

4. The multi-dimensional vehicle dispatching method of claim 3, wherein, After the preliminary determination of vehicle aggregation, it further includes: Fusing the DeepSORT tracking algorithm to continuously track the trajectories of the transport vehicles in the cluster, if the number of transport vehicles in the cluster continuously exceeds the threshold range within a set time window, it is confirmed that the vehicles are aggregated, otherwise it is determined that the aggregation is instantaneous and the aggregation determination is cancelled, and congestion warning and dispersion scheduling reminder information is generated according to the final aggregation determination result.

5. The multi-dimensional vehicle scheduling method of claim 1, wherein The congestion warning and dispersion scheduling reminder information is pushed to the corresponding normal transport vehicle through the vehicle terminal instrument or mobile terminal APP and displayed; Users feed back confirmation information or abnormal report information through the vehicle terminal instrument or mobile terminal APP.

6. A multi-dimensional vehicle dispatching apparatus characterized by comprising: The device includes the method of any one of claims 1-5: An information collection and mapping module collects basic information and state information through Internet of Things terminals and industrial bus integration, and establishes a mapping relationship between the unique device ID of the transport vehicle and the digital twin of the fixed facility, including loading and unloading points, maintenance stations, charging and battery swapping stations, and fueling stations; A transport vehicle path planning module obtains information of online transport vehicles, re-plans the path of abnormal transport vehicles having information that does not affect driving, and calculates the shortest path from the abnormal transport vehicle to the available fixed facility using the Floyd-Warshall algorithm, wherein the path calculation excludes heavy load uphill sections with a slope exceeding a preset threshold; A congestion detection and scheduling module is triggered when the transport vehicle has information affecting driving, the congestion detection and scheduling module analyzes the spatial distribution of the online normal transport vehicle position point set based on the DBSCAN density clustering algorithm, and when the vehicle density of a particular line exceeds a preset threshold, congestion warning and dispersion scheduling reminder information is pushed. An information interaction module pushes the above-mentioned reminding information through a vehicle terminal or a mobile terminal APP, and receives user feedback confirmation or abnormal reporting information; The transport vehicle path planning module comprises: An abnormal transport vehicle path dynamic planning unit uses a Floyd-Warshall algorithm to calculate the shortest path, and sequentially performs: 1) taking the loading and unloading points, maintenance stations, charging and battery swapping stations and refueling stations as path nodes, taking the passable road sections between the nodes as path edges, and assigning an initial weight to each edge, which is the actual length of the road section; If the road section is a non-hardened road, the weight of the road section is adjusted to W = L × 1.3, if it is a hardened road, further judgment is made, if the slope of a road section is α ≥ 5% and the transport vehicle is in heavy load uphill carrying mode, the weight of the road section is corrected: ， Otherwise, the original length is maintained W = L ; wherein, W is the modified link weight, L is the original link length, α is the slope; 2) initializing a distance matrix, each element in the matrix representing the initial distance from the corresponding starting point to the end point, the distance between the same nodes being 0, the distance between the nodes connected by the direct road section being the weight of the corresponding road section, and the distance between the nodes without direct connection being infinite; Starting from the first node, each node is sequentially taken as an intermediate node, and the shortest path from all starting points to the end point is recalculated, and if the path through the intermediate node is shorter than the previously recorded path, the distance matrix is updated; When the distance matrix no longer changes after three consecutive iterations, the calculation is ended in advance; When the distance matrix of the 1 k st iteration is identical to the distance matrix of the 1 k st, k nd, k th iteration, the calculation is stopped, k ≥ 3; 3) extracting the shortest path from the final distance matrix from the current position of the transport vehicle to the target station; Automatic exclusion of slopes in the route α ≥ 8% and the transport vehicle is in a heavy uphill load mode The final path information is sent to the vehicle terminal of the abnormal transport vehicle; A normal transport vehicle path dynamic planning unit dynamically plans the transport path of the normal transport vehicle, and sequentially performs: a) calculating a safety envelope space based on the speed of the normal transport vehicle, the safety envelope space being a long strip-shaped area with the normal transport vehicle as the center, extending 10 seconds of driving distance forward, 10 meters backward, and 5 meters left and right; b) marking the abnormal transport vehicle as a virtual obstacle, and calculating a virtual obstacle space, which is a long strip-shaped area with the driving area predicted based on the speed of the abnormal transport vehicle for 30 seconds as the center, and the length and width of the long strip-shaped area being twice the abnormal transport vehicle, the abnormal transport vehicle being an abnormal transport vehicle with a fault, an electric quantity less than 30%, or an oil level less than 30%; c) when the safety envelope space of the normal transport vehicle overlaps with the spatial range of any virtual obstacle, then the transport path of the normal transport vehicle is corrected, a detour transport path is calculated based on the A* algorithm, the A* algorithm is calculated by the function f ( n ) = g ( n ) + h ( n ) real-time update planning path, form a detour path: wherein, n is the path node number, i is the road segment number of the mining area, 1≤ i ≤ m , L i is the actual length of the road segment i , w i is the road surface weight, 0.8 for hardened road surface w i = 1.2 for non-hardened road surface w i , α i is the slope of the road segment i , d (n，终点) is the Euclidean distance from the node n to the end point; The detour path needs to meet: The minimum distance of the detour path to the virtual obstacle space is ≥ 10 meters, and the f ( n ) minimum.

7. The multi-dimensional vehicle dispatching apparatus according to claim 6, wherein The congestion detection and scheduling module comprises: A clustering analysis unit sets a neighborhood radius range and a minimum point number range, marks the position point of any transport vehicle with a number of transport vehicles in the neighborhood radius range greater than or equal to the minimum point number range as a core point, combines all core points associated by direct or indirect connection to form a cluster, and when the number of transport vehicles in the cluster exceeds a threshold range, preliminarily determines that the vehicles are aggregated, and sends congestion warning and dispersion scheduling reminding information; A tracking determination unit is triggered when the number of transport vehicles in the cluster exceeds the threshold range, fuses the DeepSORT tracking algorithm for continuous trajectory tracking of the transport vehicles in the cluster, and if the number of transport vehicles in the cluster continuously exceeds the threshold within a set time window, it is determined that the vehicles are aggregated, otherwise it is determined that the vehicles are temporarily aggregated and the determination is cancelled, and congestion warning and dispersion scheduling reminding information is generated according to the final aggregation determination result.

8. An electronic device, characterized by Comprise: At least one processor, and a memory connected with the at least one processor in communication, wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to cause the at least one processor to perform the method of any one of claims 1-5.

9. A storage medium having stored thereon a computer program, characterized in that The program is executed by the processor to implement the method of any one of claims 1-5.

10. Computer program product comprising a computer program, characterized in that, The program is executed by the processor to implement the method of any one of claims 1-5.

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